Ontology type: schema:Chapter Open Access: True
2018
AUTHORSDwarikanath Mahapatra , Behzad Bozorgtabar , Jean-Philippe Thiran , Mauricio Reyes
ABSTRACTTraining robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about \(35\%\) of the full dataset, thus saving significant time and effort over conventional methods. More... »
PAGES580-588
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
ISBN
978-3-030-00933-5
978-3-030-00934-2
http://scigraph.springernature.com/pub.10.1007/978-3-030-00934-2_65
DOIhttp://dx.doi.org/10.1007/978-3-030-00934-2_65
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